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To really learn data science, you should not only master
the tools—data science libraries, frameworks, modules,
and toolkits—but also understand the ideas and principles
underlying them. Updated for Python 3.6, this second edition
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Table of Contents
Preface to the Second Edition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Preface to the First Edition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv
1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
The Ascendance of Data
What Is Data Science?
Motivating Hypothetical: DataSciencester
Finding Key Connectors
Data Scientists You May Know
Salaries and Experience
Paid Accounts
Topics of Interest
Onward
1
1
2
3
6
8
11
11
13
2. A Crash Course in Python. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
The Zen of Python
Getting Python
Virtual Environments
Whitespace Formatting
Modules
Functions
Strings
Exceptions
Lists
Tuples
Dictionaries
defaultdict
15
16
16
17
19
20
21
21
22
23
24
25
iii
Counters
Sets
Control Flow
Truthiness
Sorting
List Comprehensions
Automated Testing and assert
Object-Oriented Programming
Iterables and Generators
Randomness
Regular Expressions
Functional Programming
zip and Argument Unpacking
args and kwargs
Type Annotations
How to Write Type Annotations
Welcome to DataSciencester!
For Further Exploration
26
26
27
28
29
30
30
31
33
35
36
36
36
37
38
41
42
42
3. Visualizing Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
matplotlib
Bar Charts
Line Charts
Scatterplots
For Further Exploration
43
45
49
50
52
4. Linear Algebra. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Vectors
Matrices
For Further Exploration
55
59
62
5. Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Describing a Single Set of Data
Central Tendencies
Dispersion
Correlation
Simpson’s Paradox
Some Other Correlational Caveats
Correlation and Causation
For Further Exploration
iv
|
Table of Contents
63
65
67
68
71
72
73
73
6. Probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Dependence and Independence
Conditional Probability
Bayes’s Theorem
Random Variables
Continuous Distributions
The Normal Distribution
The Central Limit Theorem
For Further Exploration
75
76
78
79
80
81
84
86
7. Hypothesis and Inference. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Statistical Hypothesis Testing
Example: Flipping a Coin
p-Values
Confidence Intervals
p-Hacking
Example: Running an A/B Test
Bayesian Inference
For Further Exploration
87
87
90
92
93
94
95
99
8. Gradient Descent. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
The Idea Behind Gradient Descent
Estimating the Gradient
Using the Gradient
Choosing the Right Step Size
Using Gradient Descent to Fit Models
Minibatch and Stochastic Gradient Descent
For Further Exploration
101
102
105
106
106
108
109
9. Getting Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
stdin and stdout
Reading Files
The Basics of Text Files
Delimited Files
Scraping the Web
HTML and the Parsing Thereof
Example: Keeping Tabs on Congress
Using APIs
JSON and XML
Using an Unauthenticated API
Finding APIs
Example: Using the Twitter APIs
111
113
113
115
116
117
118
121
121
122
123
124
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|
v
Getting Credentials
For Further Exploration
124
128
10. Working with Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
Exploring Your Data
Exploring One-Dimensional Data
Two Dimensions
Many Dimensions
Using NamedTuples
Dataclasses
Cleaning and Munging
Manipulating Data
Rescaling
An Aside: tqdm
Dimensionality Reduction
For Further Exploration
129
129
131
133
135
137
138
140
142
144
146
151
11. Machine Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
Modeling
What Is Machine Learning?
Overfitting and Underfitting
Correctness
The Bias-Variance Tradeoff
Feature Extraction and Selection
For Further Exploration
153
154
155
157
160
161
163
12. k-Nearest Neighbors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
The Model
Example: The Iris Dataset
The Curse of Dimensionality
For Further Exploration
165
167
170
174
13. Naive Bayes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
A Really Dumb Spam Filter
A More Sophisticated Spam Filter
Implementation
Testing Our Model
Using Our Model
For Further Exploration
175
176
178
180
181
183
14. Simple Linear Regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
The Model
vi
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185
Using Gradient Descent
Maximum Likelihood Estimation
For Further Exploration
189
190
190
15. Multiple Regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
The Model
Further Assumptions of the Least Squares Model
Fitting the Model
Interpreting the Model
Goodness of Fit
Digression: The Bootstrap
Standard Errors of Regression Coefficients
Regularization
For Further Exploration
191
192
193
195
196
196
198
200
202
16. Logistic Regression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
The Problem
The Logistic Function
Applying the Model
Goodness of Fit
Support Vector Machines
For Further Investigation
203
206
208
209
210
214
17. Decision Trees. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
What Is a Decision Tree?
Entropy
The Entropy of a Partition
Creating a Decision Tree
Putting It All Together
Random Forests
For Further Exploration
215
217
219
220
223
225
226
18. Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
Perceptrons
Feed-Forward Neural Networks
Backpropagation
Example: Fizz Buzz
For Further Exploration
227
230
233
235
238
19. Deep Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
The Tensor
The Layer Abstraction
239
242
Table of Contents
|
vii
The Linear Layer
Neural Networks as a Sequence of Layers
Loss and Optimization
Example: XOR Revisited
Other Activation Functions
Example: FizzBuzz Revisited
Softmaxes and Cross-Entropy
Dropout
Example: MNIST
Saving and Loading Models
For Further Exploration
243
246
247
250
251
252
253
256
257
261
262
20. Clustering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263
The Idea
The Model
Example: Meetups
Choosing k
Example: Clustering Colors
Bottom-Up Hierarchical Clustering
For Further Exploration
263
264
266
268
269
271
277
21. Natural Language Processing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
Word Clouds
n-Gram Language Models
Grammars
An Aside: Gibbs Sampling
Topic Modeling
Word Vectors
Recurrent Neural Networks
Example: Using a Character-Level RNN
For Further Exploration
279
281
284
286
288
293
301
304
308
22. Network Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
Betweenness Centrality
Eigenvector Centrality
Matrix Multiplication
Centrality
Directed Graphs and PageRank
For Further Exploration
309
314
314
316
318
320
23. Recommender Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321
Manual Curation
viii
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Table of Contents
322
Recommending What’s Popular
User-Based Collaborative Filtering
Item-Based Collaborative Filtering
Matrix Factorization
For Further Exploration
322
323
326
328
333
24. Databases and SQL. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335
CREATE TABLE and INSERT
UPDATE
DELETE
SELECT
GROUP BY
ORDER BY
JOIN
Subqueries
Indexes
Query Optimization
NoSQL
For Further Exploration
335
338
339
340
342
345
346
348
349
349
350
350
25. MapReduce. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351
Example: Word Count
Why MapReduce?
MapReduce More Generally
Example: Analyzing Status Updates
Example: Matrix Multiplication
An Aside: Combiners
For Further Exploration
352
353
354
355
357
359
359
26. Data Ethics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361
What Is Data Ethics?
No, Really, What Is Data Ethics?
Should I Care About Data Ethics?
Building Bad Data Products
Trading Off Accuracy and Fairness
Collaboration
Interpretability
Recommendations
Biased Data
Data Protection
In Summary
For Further Exploration
361
362
362
363
364
365
365
366
367
368
368
369
Table of Contents
|
ix
27. Go Forth and Do Data Science. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371
IPython
Mathematics
Not from Scratch
NumPy
pandas
scikit-learn
Visualization
R
Deep Learning
Find Data
Do Data Science
Hacker News
Fire Trucks
T-Shirts
Tweets on a Globe
And You?
371
372
372
372
372
373
373
373
374
374
375
375
375
375
376
376
Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377
x
|
Table of Contents
Preface to the Second Edition
I am exceptionally proud of the first edition of Data Science from Scratch. It turned
out very much the book I wanted it to be. But several years of developments in data
science, of progress in the Python ecosystem, and of personal growth as a developer
and educator have changed what I think a first book in data science should look like.
In life, there are no do-overs. In writing, however, there are second editions.
Accordingly, I’ve rewritten all the code and examples using Python 3.6 (and many of
its newly introduced features, like type annotations). I’ve woven into the book an
emphasis on writing clean code. I’ve replaced some of the first edition’s toy examples
with more realistic ones using “real” datasets. I’ve added new material on topics such
as deep learning, statistics, and natural language processing, corresponding to things
that today’s data scientists are likely to be working with. (I’ve also removed some
material that seems less relevant.) And I’ve gone over the book with a fine-toothed
comb, fixing bugs, rewriting explanations that are less clear than they could be, and
freshening up some of the jokes.
The first edition was a great book, and this edition is even better. Enjoy!
Joel Grus
Seattle, WA
2019
Conventions Used in This Book
The following typographical conventions are used in this book:
Italic
Indicates new terms, URLs, email addresses, filenames, and file extensions.
xi
Constant width
Used for program listings, as well as within paragraphs to refer to program ele‐
ments such as variable or function names, databases, data types, environment
variables, statements, and keywords.
Constant width bold
Shows commands or other text that should be typed literally by the user.
Constant width italic
Shows text that should be replaced with user-supplied values or by values deter‐
mined by context.
This element signifies a tip or suggestion.
This element signifies a general note.
This element indicates a warning or caution.
Using Code Examples
Supplemental material (code examples, exercises, etc.) is available for download at
https://github.com/joelgrus/data-science-from-scratch.
This book is here to help you get your job done. In general, if example code is offered
with this book, you may use it in your programs and documentation. You do not
need to contact us for permission unless you’re reproducing a significant portion of
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Preface to the Second Edition
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xiii
Acknowledgments
First, I would like to thank Mike Loukides for accepting my proposal for this book
(and for insisting that I pare it down to a reasonable size). It would have been very
easy for him to say, “Who’s this person who keeps emailing me sample chapters, and
how do I get him to go away?” I’m grateful he didn’t. I’d also like to thank my editors,
Michele Cronin and Marie Beaugureau, for guiding me through the publishing pro‐
cess and getting the book in a much better state than I ever would have gotten it on
my own.
I couldn’t have written this book if I’d never learned data science, and I probably
wouldn’t have learned data science if not for the influence of Dave Hsu, Igor Tatari‐
nov, John Rauser, and the rest of the Farecast gang. (So long ago that it wasn’t even
called data science at the time!) The good folks at Coursera and DataTau deserve a lot
of credit, too.
I am also grateful to my beta readers and reviewers. Jay Fundling found a ton of mis‐
takes and pointed out many unclear explanations, and the book is much better (and
much more correct) thanks to him. Debashis Ghosh is a hero for sanity-checking all
of my statistics. Andrew Musselman suggested toning down the “people who prefer R
to Python are moral reprobates” aspect of the book, which I think ended up being
pretty good advice. Trey Causey, Ryan Matthew Balfanz, Loris Mularoni, Núria Pujol,
Rob Jefferson, Mary Pat Campbell, Zach Geary, Denise Mauldin, Jimmy O’Donnell,
and Wendy Grus also provided invaluable feedback. Thanks to everyone who read
the first edition and helped make this a better book. Any errors remaining are of
course my responsibility.
I owe a lot to the Twitter #datascience commmunity, for exposing me to a ton of new
concepts, introducing me to a lot of great people, and making me feel like enough of
an underachiever that I went out and wrote a book to compensate. Special thanks to
Trey Causey (again), for (inadvertently) reminding me to include a chapter on linear
algebra, and to Sean J. Taylor, for (inadvertently) pointing out a couple of huge gaps
in the “Working with Data” chapter.
Above all, I owe immense thanks to Ganga and Madeline. The only thing harder than
writing a book is living with someone who’s writing a book, and I couldn’t have pulled
it off without their support.
xiv
|
Preface to the Second Edition
If you have an aptitude for mathematics and some
programming skills, author Joel Grus will help you get
comfortable with the math and statistics at the core of data
science, and with the hacking skills you need to get started as
a data scientist. Packed with new material on deep learning,
statistics, and natural language processing, this updated book
shows you how to find the gems in today’s messy glut of data.
• Get a crash course in Python
• Learn the basics of linear algebra, statistics, and probability—
and how and when they’re used in data science
• Collect, explore, clean, munge, and manipulate data
• Dive into the fundamentals of machine learning
• Implement models such as k-nearest neighbors, Naive
Bayes, linear and logistic regression, decision trees, neural
networks, and clustering
“Joel takes you on a
journey from being datacurious to getting a
thorough understanding
of the bread-and-butter
algorithms that every data
scientist should know.”
—Rohit Sivaprasad
Engineer, Facebook
“I’ve recommended Data
Science from Scratch to
analysts and engineers
wanting to make the
jump into machine
learning. It’s the best
tool for understanding
the fundamentals of the
discipline.”
—Tom Marthaler
Engineering Manager, Amazon
• Explore recommender systems, natural language
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Machine Learning Engineer, Grubhub
DATA / DATA SCIENCE
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Grus
Joel Grus is a research engineer at the Allen Institute for Artificial
Intelligence. Previously he worked as a software engineer at Google
and as a data scientist at several startups. He lives in Seattle,
where he regularly attends data science happy hours. He blogs
infrequently at joelgrus.com and tweets all day long at @joelgrus.
“Translating data science
concepts into code is
hard. Joel’s book makes it
much easier.”
Data Science from Scratch
Data Science from Scratch
To really learn data science, you should not only master
the tools—data science libraries, frameworks, modules,
and toolkits—but also understand the ideas and principles
underlying them. Updated for Python 3.6, this second edition
of Data Science from Scratch shows you how these tools and
algorithms work by implementing them from scratch.
nd
co ion
Se dit
E
SECOND
EDITION
Data Science
from Scratch
First Principles with Python
ay
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Second Edition/2019/Paperback/English
Joel Grus